The number of Language Models (LMs) dedicated to processing scientific text is on the rise.
Keeping pace with the rapid growth of scientific LMs (SciLMs) has become a daunting task …

To address the scarcity of massive labeled data, cross-domain named entity recognition
(cross-domain NER) attracts increasing attention. Recent studies focus on decomposing …

As the categories of named entities rapidly increase, the deployed NER models are required
to keep updating toward recognizing more entity types, creating a demand for class …

Many models that leverage knowledge graphs (KGs) have recently demonstrated
remarkable success in question answering (QA) tasks. In the real world, many facts …

Recent studies have shown remarkable success in cross-domain named entity recognition
(cross-domain NER). Despite the promising results, existing methods mainly utilize pre …

Benefiting many information retrieval applications, named entity recognition (NER) has
shown impressive progress. Recently, there has been a growing trend to decompose …

Although artificial intelligence (AI) has achieved many feats at a rapid pace, there still exist
open problems and fundamental shortcomings related to performance and resource …

Cross-domain Named Entity Recognition (NER) transfers knowledge learned from a rich-
resource source domain to improve the learning in a low-resource target domain. Most …

Abstract Knowledge graph construction which aims to extract knowledge from the text
corpus, has appealed to the NLP community researchers. Previous decades have witnessed …

The Transformer-based model have made significant strides in semantic matching tasks by
capturing connections between phrase pairs. However, to assess the relevance of sentence …